• Tuesday, January 9th 2018 at 16:00 - 17:00 UTC (Other timezones)
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I will talk about two recently published computational psychiatry studies. In both of them, we used Bayesian modelling of time series to reveal and quantify aberrant inference, that is, inference which does not adequately reflect external reality. In the first study, participants were induced by Pavlovian conditioning to hallucinate tones. Four groups of participants were recruited which differed across two dimensions: hallucinators and non-hallucinators; and patients with psychosis and non-patients. Computational modelling of behaviour revealed, among other findings, that hallucinators were more strongly influenced by top-down expectations relative to bottom-up input. fMRI revealed that areas which were independently shown to be involved in auditory-vocal hallucinations were more active in no-tone trials where hearing a tone was (falsely) reported compared to no-tone trials where no tone was reported. In the second study, participants did an association learning task with several association reversals. Bayesian modelling of reaction times revealed that participants with autism spectrum disorders (ASD) had a tendency to| overlearn about volatility in the face of environmental change and a corresponding reduction in learning about probabilistically aberrant events. Furthermore, participant-specific modeled estimates of surprise about environmental conditions were linked to pupil size in the ASD group, thus suggesting heightened noradrenergic responsivity in line with compromised neural gain.


Christoph Mathys
Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Trieste, Italy

Christoph Mathys – Using Bayesian time series models to reveal and quantify aberrant inference

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